



from ...configuration_utils import PretrainedConfig
from ...utils import logging


a      :  Union[str, Any]			     =			logging.get_logger(__name__)

a      :  Tuple			     =			{
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}







class 				_a				(   _lowerCAmelCase					):
			A      		=	'''mra'''

			def __init__(self,   SCREAMING_SNAKE_CASE_=50265,   SCREAMING_SNAKE_CASE_=768,   SCREAMING_SNAKE_CASE_=12,   SCREAMING_SNAKE_CASE_=12,   SCREAMING_SNAKE_CASE_=3072,   SCREAMING_SNAKE_CASE_="gelu",   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=512,   SCREAMING_SNAKE_CASE_=1,   SCREAMING_SNAKE_CASE_=0.0_2,   SCREAMING_SNAKE_CASE_=1E-5,   SCREAMING_SNAKE_CASE_="absolute",   SCREAMING_SNAKE_CASE_=4,   SCREAMING_SNAKE_CASE_="full",   SCREAMING_SNAKE_CASE_=0,   SCREAMING_SNAKE_CASE_=0,   SCREAMING_SNAKE_CASE_=1,   SCREAMING_SNAKE_CASE_=0,   SCREAMING_SNAKE_CASE_=2,   **SCREAMING_SNAKE_CASE_,   )       ->   str:
						super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,   bos_token_id=SCREAMING_SNAKE_CASE_,   eos_token_id=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Tuple							      =     vocab_size
						UpperCAmelCase_:		Any							      =     max_position_embeddings
						UpperCAmelCase_:		Any							      =     hidden_size
						UpperCAmelCase_:		List[Any]							      =     num_hidden_layers
						UpperCAmelCase_:		Union[str, Any]							      =     num_attention_heads
						UpperCAmelCase_:		Any							      =     intermediate_size
						UpperCAmelCase_:		Optional[Any]							      =     hidden_act
						UpperCAmelCase_:		Any							      =     hidden_dropout_prob
						UpperCAmelCase_:		Union[str, Any]							      =     attention_probs_dropout_prob
						UpperCAmelCase_:		Any							      =     initializer_range
						UpperCAmelCase_:		Dict							      =     type_vocab_size
						UpperCAmelCase_:		int							      =     layer_norm_eps
						UpperCAmelCase_:		Any							      =     position_embedding_type
						UpperCAmelCase_:		Any							      =     block_per_row
						UpperCAmelCase_:		Tuple							      =     approx_mode
						UpperCAmelCase_:		Tuple							      =     initial_prior_first_n_blocks
						UpperCAmelCase_:		Tuple							      =     initial_prior_diagonal_n_blocks











import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


a      :  Optional[int]			     =			logging.getLogger(__name__)







@dataclass
class 				_a				(   _lowerCAmelCase					):
			A      		=	field(
			    default=0.0 ,		metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to SortishSamler or not.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''whether to use adafactor'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default='''linear''' ,		metadata={'''help''': F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()					)}'''} ,		)






